one noise variable, linear regression
## [1] "*************************************************************"
## [1] "one noise variable, linear regression"
## [1] "bSigmaBest 38"
## [1] "naive effects model"
## [1] "one noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2322 -0.6020 0.0120 0.5804 3.2574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001467 0.019623 0.075 0.94
## n1 1.000321 0.038697 25.850 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8776 on 1998 degrees of freedom
## Multiple R-squared: 0.2506, Adjusted R-squared: 0.2503
## F-statistic: 668.2 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.87711349635425"
## [1] " application rmse 1.15239485807949"
## [1] "one noise variable, linear regression naive effects model train rmse 0.87711349635425"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.140]
## [1] "one noise variable, linear regression naive effects model test rmse 1.15239485807949"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.293]
## [1] "effects model, sigma= 38"
## [1] "one noise variable, linear regression effects model, sigma= 38 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4173 -0.7012 -0.0023 0.6639 3.9036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001463 0.022626 0.065 0.94844
## n1 0.003523 0.001301 2.708 0.00683 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 1998 degrees of freedom
## Multiple R-squared: 0.003657, Adjusted R-squared: 0.003158
## F-statistic: 7.333 on 1 and 1998 DF, p-value: 0.006828
##
## [1] " train rmse 1.01137387225598"
## [1] " application rmse 1.00160641577984"
## [1] "one noise variable, linear regression Noised 38 train rmse 1.01137387225598"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.446]
## [1] "one noise variable, linear regression Noised 38 test rmse 1.00160641577984"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.599]
## [1] "effects model, jacknifed"
## [1] "one noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4251 -0.6776 -0.0009 0.6645 3.8913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001465 0.022668 0.065 0.948
## n1 0.004279 0.038189 0.112 0.911
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 6.285e-06, Adjusted R-squared: -0.0004942
## F-statistic: 0.01256 on 1 and 1998 DF, p-value: 0.9108
##
## [1] " train rmse 1.01322491166252"
## [1] " application rmse 0.99567998170435"
## [1] "one noise variable, linear regression jackknifed train rmse 1.01322491166252"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.752]
## [1] "one noise variable, linear regression jackknifed test rmse 0.99567998170435"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.905]
## [1] "********"
## [1] "one noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9814 0.9961 1.0010 1.0000 1.0050 1.0210
## [1] 0.007055417
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.123 1.142 1.149 1.151 1.160 1.184
## [1] 0.01369983
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9827 0.9969 1.0010 1.0010 1.0060 1.0230
## [1] 0.007141001
## [1] "********"
## [1] "*************************************************************"
one variable, linear regression
## [1] "*************************************************************"
## [1] "one variable, linear regression"
## [1] "bSigmaBest 1"
## [1] "naive effects model"
## [1] "one variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3721 -0.6891 -0.0037 0.6848 3.7826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20623 0.02260 9.125 <2e-16 ***
## x1 1.00000 0.03685 27.137 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2693, Adjusted R-squared: 0.269
## F-statistic: 736.4 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01025938596012"
## [1] " application rmse 0.999915402747535"
## [1] "one variable, linear regression naive effects model train rmse 1.01025938596012"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1348]
## [1] "one variable, linear regression naive effects model test rmse 0.999915402747535"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1501]
## [1] "effects model, sigma= 1"
## [1] "one variable, linear regression effects model, sigma= 1 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3713 -0.6877 -0.0036 0.6870 3.7765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20623 0.02260 9.124 <2e-16 ***
## x1 0.99980 0.03685 27.135 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2693, Adjusted R-squared: 0.2689
## F-statistic: 736.3 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01028359193861"
## [1] " application rmse 1.0002814889679"
## [1] "one variable, linear regression Noised 1 train rmse 1.01028359193861"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1654]
## [1] "one variable, linear regression Noised 1 test rmse 1.0002814889679"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1807]
## [1] "effects model, jacknifed"
## [1] "one variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3933 -0.6946 -0.0039 0.6875 3.7985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2062 0.0227 9.084 <2e-16 ***
## x1 0.9871 0.0370 26.682 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.015 on 1998 degrees of freedom
## Multiple R-squared: 0.2627, Adjusted R-squared: 0.2623
## F-statistic: 712 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01481235978284"
## [1] " application rmse 1.00008428967326"
## [1] "one variable, linear regression jackknifed train rmse 1.01481235978284"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1960]
## [1] "one variable, linear regression jackknifed test rmse 1.00008428967326"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2113]
## [1] "********"
## [1] "one variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9861 0.9977 1.0030 1.0030 1.0090 1.0230
## [1] 0.007454232
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9857 0.9978 1.0030 1.0030 1.0090 1.0230
## [1] 0.007472658
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9855 0.9981 1.0030 1.0040 1.0100 1.0230
## [1] 0.007588317
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, linear regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, linear regression"
## [1] "bSigmaBest 10"
## [1] "naive effects model"
## [1] "one variable plus noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9216 -0.6181 0.0055 0.6225 3.5298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20622 0.02058 10.02 <2e-16 ***
## x1 0.83459 0.03452 24.17 <2e-16 ***
## n1 0.78131 0.03844 20.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9203 on 1997 degrees of freedom
## Multiple R-squared: 0.3946, Adjusted R-squared: 0.394
## F-statistic: 650.8 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.919591353886876"
## [1] " application rmse 1.12246743812363"
## [1] "one variable plus noise variable, linear regression naive effects model train rmse 0.919591353886876"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2556]
## [1] "one variable plus noise variable, linear regression naive effects model test rmse 1.12246743812363"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2709]
## [1] "effects model, sigma= 10"
## [1] "one variable plus noise variable, linear regression effects model, sigma= 10 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3777 -0.6850 -0.0026 0.6743 3.8148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.206228 0.022570 9.137 < 2e-16 ***
## x1 0.972540 0.035822 27.149 < 2e-16 ***
## n1 0.017061 0.005321 3.207 0.00136 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.009 on 1997 degrees of freedom
## Multiple R-squared: 0.2717, Adjusted R-squared: 0.271
## F-statistic: 372.6 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.00858611644105"
## [1] " application rmse 1.01268889785082"
## [1] "one variable plus noise variable, linear regression Noised 10 train rmse 1.00858611644105"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2862]
## [1] "one variable plus noise variable, linear regression Noised 10 test rmse 1.01268889785082"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3015]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3986 -0.6920 -0.0077 0.6877 3.8126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20643 0.02268 9.101 <2e-16 ***
## x1 0.98425 0.03698 26.614 <2e-16 ***
## n1 -0.07739 0.03479 -2.224 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 1997 degrees of freedom
## Multiple R-squared: 0.2645, Adjusted R-squared: 0.2638
## F-statistic: 359.2 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01355772650768"
## [1] " application rmse 1.00913108707443"
## [1] "one variable plus noise variable, linear regression jackknifed train rmse 1.01355772650768"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3168]
## [1] "one variable plus noise variable, linear regression jackknifed test rmse 1.00913108707443"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3321]
## [1] "********"
## [1] "one variable plus noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9923 0.9994 1.0040 1.0050 1.0090 1.0200
## [1] 0.006676811
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.104 1.125 1.134 1.134 1.142 1.168
## [1] 0.01460334
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9952 1.0050 1.0100 1.0100 1.0150 1.0380
## [1] 0.008087424
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, diagonal regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, diagonal regression"
## [1] "bSigmaBest 12"
## [1] "naive effects model"
## [1] "one variable plus noise variable, diagonal regression naive effects model fit model:"
## x1 n1
## 1.000005 1.000333
## [1] " train rmse 0.958540237968956"
## [1] " application rmse 1.20618715828122"
## [1] "one variable plus noise variable, diagonal regression naive effects model train rmse 0.958540237968956"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3764]
## [1] "one variable plus noise variable, diagonal regression naive effects model test rmse 1.20618715828122"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3917]
## [1] "effects model, sigma= 12"
## [1] "one variable plus noise variable, diagonal regression effects model, sigma= 12 fit model:"
## x1 n1
## 0.96427681 0.01029148
## [1] " train rmse 1.03047987309954"
## [1] " application rmse 1.03262192424165"
## [1] "one variable plus noise variable, diagonal regression Noised 12 train rmse 1.03047987309954"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4070]
## [1] "one variable plus noise variable, diagonal regression Noised 12 test rmse 1.03262192424165"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4223]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, diagonal regression effects model, jackknifed fit model:"
## x1 n1
## 0.9871528 -0.1088369
## [1] " train rmse 1.03458802692346"
## [1] " application rmse 1.03176880530955"
## [1] "one variable plus noise variable, diagonal regression jackknifed train rmse 1.03458802692346"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4376]
## [1] "one variable plus noise variable, diagonal regression jackknifed test rmse 1.03176880530955"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4529]
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.008 1.018 1.022 1.022 1.028 1.039
## [1] 0.006486922
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.177 1.209 1.220 1.221 1.233 1.259
## [1] 0.01738037
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.015 1.023 1.031 1.030 1.036 1.077
## [1] 0.009058402
## [1] "********"
## [1] "*************************************************************"